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Record W3123839103 · doi:10.1111/1911-3846.12586

The Effects of Creative Culture on Real Earnings Management*

2020· article· en· W3123839103 on OpenAlexvenueno aff
Ryan Guggenmos, Wim A. Van der Stede

Bibliographic record

VenueContemporary Accounting Research · 2020
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionCreativityContext (archaeology)Intervention (counseling)Construal level theoryAffect (linguistics)Earnings managementPsychologyOrganizational cultureEarningsUnintended consequencesSocial psychologyPublic relationsMarketingBusinessAccountingPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT Creativity and innovation have been identified by senior executives as some of the most desired characteristics of corporate culture. Accordingly, managers strive to build these cultures within their organizations. However, research in psychology suggests that these attempts may have unintended negative consequences. In this study, I predict and find that managers in a more (versus less) innovative company culture will engage in higher levels of real earnings management (REM). I then test two construal level theory (CLT)‐based interventions designed to reduce REM. As I predict, I find that in more innovative corporate cultures an intervention that makes downside risk more salient reduces REM, but an intervention that encourages managers to consider the “big‐picture” impact of their decision reduces REM to a greater extent. Unexpectedly, I also find that the effect of the “big‐picture” intervention reverses in a less innovative corporate culture leading to an increase in REM. My findings contribute to the emerging accounting literature regarding REM. I also extend the psychology literature investigating the link between opportunistic behavior and creativity, and I also expand research into how interventions based on CLT can affect judgment and decision making in an accounting context.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.566
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.055
GPT teacher head0.382
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations62
Published2020
Admission routes1
Has abstractyes

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